Method and device for utilizing analyte levels to assist in the treatment of diabetes

ABSTRACT

A health-monitoring device assesses the health of a user based on levels of two analytes in a biological fluid. A first analyte that is utilized to assess a user&#39;s health is a fat metabolism analyte, such as ketones, free fatty acids and glycerol, which is indicative of fat metabolism. A second analyte that is utilized is a glucose metabolism analyte, such as glucose. The levels of the two analytes may be used to assess insulin sensitivity, to detect both recent hypoglycemia and the cause of high glucose levels, and/or to guide therapeutic intervention.

RELATED APPLICATIONS

The present invention is a continuation in part of U.S. patentapplication Ser. No. 10/817,211 filed Apr. 1, 2004, entitled Method andDevice for Utilizing Analyte Levels to Assist in the Treatment ofDiabetes, Insulin Resistance and Metabolic Syndrome, which in turnclaims priority to U.S. Provisional Patent Application 60/459,310entitled Method and Device for Utilizing Analyte Levels to Assist in theTreatment of Diabetes, Insulin Resistance and Metabolic Syndrome, filedApr. 1, 2003, the contents of both of which are herein incorporated byreference.

FIELD OF THE INVENTION

The present invention relates to the management of metabolic syndromeand diabetes. More particularly, the present invention relates tomethods for managing therapeutic interventions in diabetes usingquantification of biochemical markers in the subject to assess the fatand glucose metabolism, insulin sensitivity as well as the past andprospective effects of a certain given medication.

BACKGROUND OF THE INVENTION

Between 1990 and 1998 the prevalence of diabetes in the United Statesrose from 4.9 to 6.5%. During the 1990's the prevalence of non-insulindependent diabetes increased by 33% overall and by 70% among people intheir thirties. Diabetes affects now sixteen million Americans. Thedirect costs resulting from diabetes is $44 billion per year, and thetotal cost of diabetes, including indirect costs, rises to $98 billionper year. 13.5% of obese patients have diabetes compared to 3.5% ofthose with a normal weight.

Diabetes is the “tip of the Iceberg” and is most often preceded by ametabolic syndrome. The prevalence of the metabolic syndrome gives anestimate of the potential magnitude of the problem. The Centers ofDisease Control and Prevention recently investigated the prevalence ofthe metabolic syndrome: The unadjusted and age-adjusted prevalences were21.8% and 23.7%, respectively. The prevalence increased from 6.7% amongparticipants aged 20 through 29 years to 43.5% and 42.0% forparticipants aged 60 through 69 years and aged at least 70 years,respectively. Using 2000 census data, about 47 million US residents havethe metabolic syndrome.

Most patients who go through the evolution of metabolic syndrome todiabetes will ultimately require insulin injections to deal with theirdisease. According to research, the well educated Type I diabetespatient encounters on average about 5 hypoglycaemic episodes and aboutthe same hyperglycaemic episodes every week. Both conditions may lead toa variety of complications, such as lack in concentration, loss ofconscience, coma, dehydration and death.

A major draw back of the algorithms used to predict glucose levels inthe prior art is that the algorithms use theoretical absorption curvesof the injected insulin. These curves try to predict the appearance ofinsulin in the bloodstream and the insulin activity which is then usedin the prediction model. The current prediction algorithms do not takeinto account other insulin-interfering factors, and are therefore oftenhighly inaccurate.

SUMMARY OF THE INVENTION

The present invention provides a comprehensive approach to themanagement of diabetes. The method of the present invention utilizesdual parameters in understanding metabolic changes in the body. A firstparameter that may be utilized in accordance with the present inventionmay comprise biochemical signals indicative of fat metabolism (e.g.,Ketones or Free Fatty Acids or Glycerol levels) and a second parametermay comprise biochemical signals indicative of glucose metabolism (e.g.,glucose levels). Furthermore, the dual analyte model allows monitoringof the progression of those disease states, as well as progress made bytherapeutic interventions. For insulin dependent diabetes in particular,the dual analyte model can help in the dosing of medication (insulin andothers) and of dietary changes.

The measurable signals may also be used to assess a real insulinactivity (the combined effect of insulin concentration, insulinsensitivity and counter-regulating hormones), to detect a discrepancy,known as an insulin activity error, between the theoretical insulinactivity and the real insulin activity. The calculated insulin activityerror allows for detection of currently unknown insulin over-activity,which usually leads to hypoglycaemia in the near term or to detectunknown insulin under-activity that may lead to hyperglycemia. In Type Idiabetes in particular, the invention can be used to alert for upcomingglucose deregulation as well as retrospectively detect abnormalregulation episodes. Through detection of upcoming events, thedual-analyte system may advise a patient to take an action, for example,to increase testing frequency or comment on the dosing of themedication.

Through the invention, free fatty acid (FFA) levels in Type I diabetesmay be used for better diabetes regulation. For example, fat metabolitelevels can be used to assess the insulin activity of a patient at agiven moment. A patient may be urged to test glucose levels earlier, forexample, in an hour (instead of four hours, when he takes his nextmeal), to help identify an anticipated problem. The present inventionemploys body fat metabolite levels to assess actual and real insulinactivity, defined as an intergration of blood insulin levels, plusinsulin sensitivity, plus the activity of the counter-regulatinghormones. Advice to a patient can take the form of a glucose predictionfor a future period and/or recommending upon the timing of the nexttest.

The present invention provides a single device for testing both a fatmetabolism analyte and a glucose metabolism analyte, as well as forinterpreting the combined results of the dual analyte measurements.

According to a first aspect of the invention, a method of assessing aninsulin activity error and its effect on glucose levels is provided. Themethod comprising the computer implemented steps of measuring an amountof a first analyte in a biological fluid sample reflecting body fatmetabolism and an amount of a second analyte in the biological fluidsample reflecting glucose metabolism, assessing a real insulin activitylevel based on the amount of the first analyte in the biological fluidsample, comparing the real insulin activity with a theoretical amount ofinsulin to calculate the insulin activity error; and assessing a glucoselevel based on the amount of the second analyte in the biological fluidand the calculated insulin activity error

According to another aspect of the invention, a method of predicting auser's glucose levels in the future is provided. The method comprisesthe computer implemented steps of measuring an amount of a first analytein a biological fluid sample reflecting body fat metabolism and anamount of a second analyte in the biological fluid sample reflectingglucose metabolism, assessing a real insulin activity level in the userbased on the amount of the first analyte and the second analyte in thebiological fluid sample, comparing the real insulin activity level witha theoretical amount of insulin to calculate an insulin activity error,utilizing an additional parameter comprising at least one of: body massindex, gender, meal intake, medication, exercise duration and intensity,alcohol consumption and weight and utilizing a relevant algorithmcorrected for the insulin activity error to model the glucose levels fora future period.

According to still another aspect of the invention, a health-monitoringdevice is provided. The health monitoring device comprises a testelement and a processor. The test element measures an amount of a firstanalyte in a biological fluid sample reflecting body fat metabolism andan amount of a second analyte in the biological fluid sample reflectingglucose metabolism. The processor assesses a real insulin activity levelof the user based on the amount of the first analyte in the biologicalfluid sample, compares the calculated real insulin activity with atheoretical amount of insulin to calculate an insulin activity error,and assesses a glucose level based on the amount of the second analytein the biological fluid and the calculated insulin activity error.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1 a and 1 b illustrate an electronic health monitoring device forsampling and analyzing a biological fluid sample and assessing thehealth of a user based on levels of two analytes in the sample.

FIG. 2 illustrates the output and user interface of the device of FIGS.1 a and 1 b when tracking an insulin resistance factor.

FIG. 3 a is a schematic of a health monitoring system including thehealth monitoring device of FIGS. 1 a and 1 b.

FIG. 3 b is a block diagram showing the components of the processor ofFIG. 3 a.

FIG. 4 shows the display of the device of FIGS. 1 a and 1 b when thedevice is used to track an intra-day evolution of glucose and FFA levelsand display a warning about imminent hypoglycemia, according to anembodiment of the invention.

FIG. 5 shows the display of the device of FIGS. 1 a and 1 b when thedevice is used to display early morning test results for glucose and FFAand the interpretation thereof, according to an embodiment of theinvention.

FIG. 6 illustrates an electronic monitoring device for sampling andanalyzing a biological fluid sample and assessing the insulin activityerror of a user based on levels of two analytes in the sample accordingto one embodiment of the invention.

FIG. 7 a illustrates the output and user interface of the device of FIG.6 when tracking the real insulin activity of a user.

FIG. 7 b shows the evolution of the insulin activity error in a userover time according to an embodiment of the invention.

FIG. 8 a displays a traditional glucose prediction curve and measuredglucose and free fatty acid values of a patient over 5 days.

FIG. 8 b illustrates the theoretical insulin activity with thecalculated real insulin activity in the same patient as in 8 a.

FIG. 8 c illustrates the impact of the insulin activity error on theglucose prediction error in the same patient as 8 a and 8 b.

FIG. 9 shows the display of the device of FIG. 6 according to anembodiment when the device is used to track an intra-day evolution ofglucose levels and the insulin activity error and display a warningabout imminent hypoglycemia.

FIG. 10 shows the display of the device of FIG. 6 according to oneembodiment of the invention, when the device is used to graphicallydisplay glucose levels and an insulin activity error.

FIG. 11 shows the display of the device of FIG. 6 according to anembodiment of the invention, when the device is used to display earlymorning test results for glucose levels and insulin activity error andthe interpretation thereof.

FIG. 12A is a chart illustrating measured results for a patient during afirst experiment conducted according to the teachings of the invention.

FIG. 12B is a graph showing a glucose prediction error, an insulinactivity error, incidents of unexpected hypoglycaemia and incidents ofexpected hypoglycaemia for the patient during the first experiment.

FIG. 13A is a chart illustrating measured results for a patient during asecond experiment conducted according to the teachings of the invention.

FIG. 13B is a graph showing a glucose prediction error, an insulinactivity error, incidents of unexpected hypoglycaemia and incidents ofexpected hypoglycaemia for the patient during the second experiment.

FIG. 14A is a chart illustrating measured results for a patient during athird experiment conducted according to the teachings of the invention.

FIG. 14B is a graph showing a glucose prediction error, an insulinactivity error, incidents of unexpected hypoglycaemia and incidents ofexpected hypoglycaemia for the patient during the third experiment.

FIG. 15A is a chart illustrating measured results for a patient during afourth experiment conducted according to the teachings of the invention.

FIG. 15B is a graph showing a glucose prediction error, an insulinactivity error, incidents of unexpected hypoglycaemia and incidents ofexpected hypoglycaemia for the patient during the fourth experiment.

FIG. 16A is a chart illustrating measured results for a patient during afifth experiment conducted according to the teachings of the invention.

FIG. 16B is a graph showing a glucose prediction error, an insulinactivity error, incidents of unexpected hypoglycaemia and incidents ofexpected hypoglycaemia for the patient during the fifth experiment.

FIG. 17A is a chart illustrating measured results for a patient during asixth experiment conducted according to the teachings of the invention.

FIG. 17B is a graph showing a glucose prediction error, an insulinactivity error, incidents of unexpected hypoglycaemia and incidents ofexpected hypoglycaemia for the patient during the sixth experiment.

FIG. 18A is a chart illustrating measured results for a patient during aseventh experiment conducted according to the teachings of theinvention.

FIG. 18B is a graph showing a glucose prediction error, an insulinactivity error, incidents of unexpected hypoglycaemia and incidents ofexpected hypoglycaemia for the patient during the seventh experiment.

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides a system and method for managing diabetesand metabolic syndrome. The system and method of the present inventiontracks dual parameters and utilizes the dual parameters to understandmetabolic changes, understand the real insulin activity in a user, andprovide management or therapeutic advice to a user. The invention willbe described below relative to illustrative embodiments. Those skilledin the art will appreciate that the present invention may be implementedin a number of different applications and embodiments and is notspecifically limited in its application to the particular embodimentsdepicted herein.

As used herein, the terms “fat analyte” and “fat metabolism analyte”refer to an analyte generated in a patient when consuming body fat forenergy supply. Fat analytes and fat metabolism analytes include, but arenot limited to, ketones, glycerol, Free Fatty Acids (FFA) and a fattyacid that is representative of the total FFA's in the system, such asPalmitate. Free Fatty Acids are a family of different fatty acids, andtraditional test systems for Free Fatty Acids measure the mostrepresentative fatty acid of the family, which is usually Palmitate.However, one skilled in the art will recognize that other fatty acidspresent in other proportions are also representative of a total FFAlevel and may also be used.

As used herein, the terms “glucose analyte” and “glucose metabolismanalyte” refer to an analyte indicative of glucose metabolism. Metabolicanalytes indicative of glucose metabolism include, but are not limitedto, glucose levels, pyruvate, glucose6phosphate and lactate.

The term “biological fluid” as used herein refers to a fluid containinga metabolic analyte, including, but not limited to blood, derivatives ofbloods, interstitial fluid, urine, a breath sample, saliva, andcombinations thereof.

As used herein, the term “insulin” or “medication” is intended toinclude any substance taken to interfere with the insulin-like activity,insulin resistance, lypolisis, insulin secretion, insulin sensitizers,Thiazolidines (TZD's). Examples include, but are not limited to,Insulin, Pioglitazone, Metformin, Glucophage and others known in theart.

As used herein, the terms “real insulin activity”, “real insulinactivity level” and RIA refer the actual net insulin effect on glucoseand fat metabolism, i.e., the actual combined effect of insulinconcentration, insulin sensitivity and counter-regulating hormones in apatient or other user.

As used herein the term “theoretical insulin level” refers to anestimated insulin level in a user based on calculations using a modelingalgorithm.

As used herein, the term “insulin activity error”, or IAE, refers to adiscrepancy between a theoretical insulin level and a real insulinactivity level found in the patient or other user.

As used herein, the term “glucose prediction error”, or GPE, refers to adiscrepancy between measured glucose levels and theoretical glucoselevels, estimated using a modeling algorithm.

FIGS. 1 a, 1 b, 2, 3 a, and 3 b, illustrate a health-monitoring deviceor monitor 10 for monitoring the health of a patient according to anillustrative embodiment of the invention. The illustrativehealth-monitoring device 10 includes a sampling device for sampling abiological fluid, such as blood, and a testing device for measuring thelevels of two analytes in the sample, for example a fat analyte and aglucose analyte, through means known in the art. The device 10 includesa processor 90, which is shown in FIGS. 3 a and 3 b, for running aprogram that uses the measured analyte levels to assess the health of auser.

In one embodiment, the device 10 correlates fat analyte and glucoseanalyte measurements to a health parameter to give the user anassessment of his health. The health-monitoring device includes adisplay 19 for displaying results to the user, as well for providing thedifferent options in tracking results and reading the advice. Forexample, as shown in FIG. 2, the illustrative device 10 calculates aninsulin sensitivity factor based on the measured levels of two analytesin a user. As shown, the health-monitoring device 10 tracks the progressof the user's insulin sensitivity factor over time to provide feedbackto the user regarding his health.

According to the illustrative embodiment, the health monitoring device10 measures a fat analyte, which is indicative of fat metabolism in theuser, and a glucose analyte, which is indicative of glucose metabolismin the biological fluid sample and uses the two measurements tocalculate a health parameter. The fat analyte may comprise free fattyacids (FFA), ketones, glycerol or any other analyte that is indicativeof lipolysis (fat breakdown) in the body.

According to another embodiment of the invention, the device 10 may usedto track an intra-day evolution of glucose and FFA levels and display awarning about imminent hypoglycemia, according to an embodiment of theinvention, as shown in FIG. 4. The device 10 may also be used to displayearly morning test results for glucose and FFA and the interpretationthereof, as shown in FIG. 5.

According to another embodiment of the invention, as shown in FIGS.6-10, the processor in the device 10 runs a program that uses theanalyte levels in a sampled biological fluid to assess a real insulinactivity level of the user. As shown in FIG. 6 the health monitoringdevice 10 of an illustrative embodiment of the invention may analyze abiological fluid sample and assesses an insulin activity error of a userover the course of a day, or other suitable time period, based on levelsof two analytes in the sample. Insulin activity error refers to adiscrepancy between a theoretical insulin level and a real insulinactivity level found in the patient. The device of the illustrativeembodiment of the invention compares the real insulin activity levelwith an assumed theoretical insulin activity to give the user anassessment of prospective effects on his glucose levels.

In another embodiment of the invention, the device 10 may measure auser's real insulin activity levels, and compare the measured levelswith theoretical insulin activity levels. For example, as shown in FIG.7 a, the display 19 may track and show the difference between atheoretical insulin activity 201 and a real insulin activity level 210over a period of time, indicated by axis 240. The difference isgraphically represented as an insulin activity error 230, which may alsobe tracked. As shown in FIG. 7 b, the insulin activity error 230 may betracked over a period of time, such as over several months, as shown. Apositive error indicates more insulin activity than assumed from thetheoretical insulin absorption curves.

Based on the assessment, the device 10 may automatically calculate andprovide advice to a patient. For example, the device may formulate testfrequency advice to the user based on measurements of the two analytelevels. The device 10 may formulate insulin dosing or other medicationdosing advice to the user, or automatically instruct the right insulindose to an insulin delivery device employed by the user.

Referring back to FIG. 1, the device 10 includes a housing 11, whichincorporates a sampling device, illustrated as a lancing device 12having a lancet, for piercing the skin of a user. The sampling device isused to yield a biological fluid sample containing one or more of theanalytes to be measured. The lancing device 12 may include a variabledepth selector 14 for setting the penetration depth of the lancet and atrigger button 13 for releasing the lancet to prick the skin. Oneskilled in the art will appreciate that the lancing device does not haveto be incorporated into the health-monitoring device 10 but can be aseparate stand alone device. Alternatively to the lancet, a hollowneedle may be used to extract the sample from or from within the skin.The sampling device may comprise any suitable means for yielding abiological fluid sample and is not limited to a lancing device or otherdevice for piercing the skin of a user.

The illustrative testing device 10 includes a test port 15, which allowsa disposable test element 17 to be inserted into the apparatus. The testelement 17 may comprises any suitable device for measuring analytes,including, but not limited to a test strip, a skin inserted device, suchas a catheter, or a measuring device that uses a non-invasive methods ofmeasurement which may not utilize a body fluid sample. The test element17 generates a signal indicative of the concentration of the testedmetabolic analytes in the sample, which can be based either on aphotometric, electrochemical analytical method or any other suitablemethod known in the art. The test port 15 may include electricalcontacts for reading the signal of an electrochemical based test stripor may hold a photometric or reflectometric cell to read the signal of aphotometric test strip. Other readers can be used in accordance with theteachings of the invention, including, but not limited to a fluorescencereader, magnetic reader, and others known to those of ordinary skill inthe art, depending on the utilized test element or assay technology.

One single test element 17 may be utilized to measure both analytes, sothat the patient has to sample only once (i.e. stick his finger toobtain a blood drop) to obtain both results. Alternatively, a differenttest element can be used for each analyte measured in the patient. Forexample, one analyte can be measured by one method (i.e.,photometrically), and the other by another method (i.e.,electrochemically), and the meter may incorporate both methods.

Based on the measured levels of the analytes in the biological sample, aprocessor in the health-monitoring device 10 calculates a healthparameter, such as a real insulin activity level, a prospective glucoselevel, and/or an insulin activity error, and provides feedback to theuser regarding the calculated health parameter.

A data communication port 16 in the housing 11 allows insertion of anelectrical connector to access the electronics in the device 10. Thisfeature can be used to download, as well as upload, data and programs.One skilled in the art will recognize that communication between theelectronics is not limited to electrical communication. Acoustic, optic(infrared), radio waves or other communication means known in the artmay be used as well.

The illustrative device 10 may include an interface button 18 fornavigating menu options presented on the display 19 or to select andconfirm data inputs and outputs.

The correlation between the measured analyte levels and the health of auser, assessed using a program stored in the device 10 of FIGS. 1 a and1 b, the real insulin activity, and/or an insulin activity errorassessed using a program stored in the device 10 of FIG. 6, will bedescribed in greater detail below.

The illustrated monitor 1 0 contains electronics, including a processor90 for reading and receiving a signal from the test element 17, shown inFIG. 3 a and 3 b. By using the calibration information for the testelement, the processor 90 can convert the measured signals generated bythe test element 17 to a concentration of each of the tested metabolicanalytes. The processor 90 provides feedback to a user based on thelevels of the first and second metabolic analyte in a biological fluidsample. The processor 90 includes a calculator 92 for determining thelevel of the first metabolic analyte, such as a fat analyte, and asecond metabolic analyte, such as a glucose analyte in the sample. Theprocessor 90 also includes a correlator 94 for correlating the levels ofthe first and second metabolic analyte to a health parameter indicativeof the user's health. Alternatively, the correlator 94 may correlate thelevels of the first and second metabolic analytes to the real insulinactivity in a user, and calculate an insulin activity error representinga discrepancy between the real insulin activity and a theoreticalinsulin activity level. The measured analyte concentration can bedisplayed on the display 19 and/or stored into memory of the monitor 10.

In one embodiment, as shown in FIG. 3 a, the monitor 10 may form part ofa health monitoring system 300. The health monitoring system comprisesthe monitor 10 and a remote site 72 having a database 74 for storingdata obtained by the monitor 10. As shown, the monitor may be connectedto the remote site 72 over a network 76.

According to an illustrative embodiment of the invention, the healthmonitoring device 10 utilizes and implements relationships between fatanalytes and glucose analytes in the body and parameters indicative ofthe health of a user. The processor 90 may be programmed to calculate ahealth parameter based on known relationships between levels of fatanalytes and glucose analytes and certain health parameters. Forexample, in the human body, levels of free fatty acids (FFA) rise whenthere is a rise in insulin action and a raise in counter-regulatinghormones. Obesity is also commonly associated with elevated plasma freefatty acid (FFA) levels, as well as with insulin resistance andhyperinsulinemia, two important cardiovascular risk factors.

Free Fatty Acids and Lipid Metabolism

A drop in insulin action and a rise in counter-regulating hormones alsotends to cause a rise in Free Fatty Acids (FFA) in the human body.Adipose tissue plays an important role in energy supply. In the absenceof sufficient glucose to meet the body's energy needs, lipolysis, i.e.,fat breakdown, supplies Free Fatty Acids for energy. Body fat is brokendown to release Free Fatty Acids (FFA) and glycerol into thecirculation. This typically occurs in the post-absorptive phase (thetime span between the digestion of a meal and the start of the nextmeal) and overnight (the longest fasting period of the day). Theregulation of lipolysis is under control of a variety of hormones,including insulin, glucagon, growth hormone (GH), epinephrine, adrenalinand cortisol.

Under caloric restriction, glucose levels in the body dropprogressively, and then stabilize. As a reaction, plasma levels ofinsulin drop while glucagon levels increase. The result of thisdecreased insulin/glucagon ratio is a lipolytic effect on the fattissue, which releases FFA into the blood stream. The FFA generally havetwo destinations: some are consumed directly by the body tissues forenergy, other enter the liver cells for ketogenesis (beta-oxidation toform ketones). In addition, glucagon will also stimulate the liberationof glucose from the liver and muscle stores to compensate for a shortageof glucose.

Besides glucagon, other hormones will try to compensate for the shortageof glucose. Growth hormone, for example, plays an important role duringthe night to ensure sufficient energy substrates are available. Growthhormone (GH) infusion in normal subjects increases glycerol and FFAconcentrations, indicating an enhanced lipolysis. The ketogenetic effectof growth hormone is explained by the increase of substrate (FFA)through enhanced lipolysis. Growth hormone secretion is typicallyincreased early in the night to compensate for dropping glucose levelsin the blood. Dropping glucose concentration and insulin levels triggersthe increase in GH secretion. In insulin dependent diabetes, GHsecretion is markedly increased, especially in adolescents and patientswith poorly controlled diabetes.

It has been suggested that there is a negative feedback loop between FFAand Growth Hormone. Lack of FFA itself may be the signal for growthhormone release despite the lag (generally about 2 hours) period betweenFFA decrease and Growth hormone increase. Glucose and FFA can not fullyreplace each other in their respective influence on growth hormone.

GH effects during the night may play an important role to the origin ofthe “dawn-phenomenon” found in diabetic patients, a low glucose levelduring the night followed by a high glucose at wake with an increasedneed for insulin. The typical increase of FFA, most often a doubling ofthe baseline levels, generally occurs between 2 and 3 hours after the GHpeak.

Adrenaline and epinephrine, two hormones produced under stressconditions also stimulate lipolysis in an attempt to ensure sufficientenergy substrates.

In summary, FFA rises due to a drop in insulin action and a raise incounter-regulating hormones. The raise in counter-regulating hormones isinfluenced by a couple triggers, which can sometimes be unpredictable,such as meal intake during the day and GH, stress and nervosa foradrenalin and epinephrine, the circulating level of insulin and theglucose concentration in the blood. Therefore, the rise in FFA's andtheir association with insulin sensitivity and glucose levels isunpredictable and justifies the need for frequent monitoring.

Lipolytic Parameters.

According to an illustrative embodiment, the monitoring devices measuresand correlates Free Fatty Acid levels to analyze a user's health, thoughone skilled in the art will recognize that any analyte that reflectslipolysis can be used. For example, other analytes, such as ketones andglycerol are also products from lipolysis, and can be used to assess theeffect of the counter-regulating hormones in the body. As describedabove, under a condition of low insulin, low glucose and highcounter-regulating hormones (such as, but not limited to Growth hormone,glucagon, cortisol, epinephrine and noradrenaline), lipolysis isstimulated to supply other sources of energy than glucose. Body fat isstored as triglycerides, which is a molecule made up of three free fattyacid (FFA) chains and one glycerol. Lipolysis will thus liberate FFA andglycerol from the fat stores into the circulation. The FFA's can enterbody cells (but not neural tissue cells), and be oxidized. FFA can alsoenter the liver mitochondria and be converted to ketones, also a sourceof energy but in high concentration those can be toxic. Glycerol willcontribute to the new formation of glucose.

FIG. 2 illustrates the use of the health-monitoring device 10 to monitora successful therapeutic effect over the course of months. As shown, thedisplay 19 of the device 10 may be used to display a graph 21, whichtracks a user's insulin resistance factor by graphing a curve 23 overtime. The graph may also display a therapeutic goal 22 (graphed as azone) which was set for the particular patient. The device 10 maycompare the user's actual insulin resistance factor with a set goal toprovide feedback and motivation to the user.

The device 10 may also be used to monitor insulin dependent diabetespatients. Insulin dependent diabetes patients are characterized, amongother elements, by a shortage or even absence of insulin. Typically,these patients are treated through self-administration of insulin.Insulin, which is injected by the patient himself, comes in differentforms: some preparations have a very fast and short action profile andare used typically to clear the carbohydrates from the blood streamafter a meal. Other preparations have a long half-life time and are usedto supply a patient with a more or less stable base amount of insulinthroughout the day and night.

It is the duty of the patient to balance the amount of these two insulintypes with the size and composition of his meals, exercise, stresslevels, sickness, and sleep and wake cycles. The goal of such atreatment is to achieve near-normal glucose levels. Some patients mayuse an insulin pump, which delivers continuously a self-selected amountof insulin through a catheter. Self-management is daunting task for theaverage person with diabetes.

A major challenge in the management of insulin dependent diabetes is toendure the night (the longest period of fasting) with close to normalglucose levels while avoiding hypoglycemia. The lack of food intake overthis period makes it difficult not to overdose insulin whilst avoidinghyperglycemia. An additional problem facing insulin dependent diabeticsis the long period that needs to be covered without an intervention,such as a glucose test, a meal or insulin injection (since the patientis asleep). Hypoglycemia at night is complicated by the absence ofexternal notice of the problem and of external intervention.

Glucose levels tend to fall in the first half of the night as theevening meal is digested and the glucose absorbed into the muscle andliver. The counter-regulating hormones, especially Growth hormone andglucagon, start to stimulate lipolysis to supply the body with FFAs andglycerol as energetic substrates for metabolism. The substitution ofGlucose by FFAs for the energy needs saves the further consumption ofglucose by muscle and other tissues, freeing up glucose for oxidation bythe neural tissues (brain, nerves) to maintain metabolism. Glycerol willcontribute to the neogenesis of glucose. Those two elements will causethe glucose level to increase by early morning. Cortisol levels increaseas well before waken up and have a similar hyperglycemic effect.

As a result, night hypoglycemia may not be recognized in the earlymorning glucose values. However the FFA levels before breakfast may giveinsight in the level of lipolysis occurring overnight, reflecting thedegree of hypoglycemia of the previous night period.

Depending on the relative imbalance between the evening and/or bedtimefood intake and the amount of injected insulin, glucose levels in themorning can vary substantially.

Thus, high glucose levels in the morning may result from a relativeoverdose of insulin the evening before. High FFA levels at wake indicatea hypoglycemia overnight. When coinciding with high glucose levels, thiscondition should not be treated with a higher insulin dose at bedtime.

A milder form of the counter-regulating hormone action is known as the“dawn-phenomenon”, a condition that occurs when a patient wakes up witha high glucose level and high ketone levels (indicative for the enhancedlipolysis) as a reaction to low overnight glucose levels. Insulindependent patients tend to require more insulin in the morning to lowertheir blood glucose than during the course of the day. This reducedinsulin sensitivity, caused by the counter-regulating hormones (even inabsence of night hypoglycemia) may be assessed by measuring FFA levelstogether with the glucose level in the morning before breakfast. FFAlevels can double at wake in the existence of the Dawn-phenomenon.

The health-monitoring device 10 may also be used to provide assistancein determining insulin dosage, based upon both the glucose levels andthe FFA levels in the user. For example, the health-monitoring device 10may be used to retrospectively assess night hypoglycemia utilizingmeasured FFA levels. As described, glucose levels alone are not ideal todose insulin. Glucose readings can be normal to very high in the morningas a consequence of hypoglycemia overnight. This situation is rathercaused by an over-dosing of insulin relative to the meal intake in theevening. These patients with high glucose and high FFA in the morningshould reduce insulin (or increase caloric intake or change mealcomposition) in the evening rather than take more insulin, which is thenatural reflex. Current practice in self-dosing of insulin lacks thecounter-regulating hormone information and works with glucose levelsalone. Most often this results in patients taking more insulin the nextevening to tackle the hyperglycemia. As a consequence, the followingnight even more severe hypolycemia and consequential hyperglycemia canbe the result. It usually takes several days to get back into control.

For example, FIG. 5 shows the display 19 of the health-monitoring device10 of FIG. 1 according to one embodiment when the device is used todisplay early morning test results for the two analytes. As shown, thedisplay 19 of FIG. 5 displays a first analyte measurement, illustratedas the free fatty acid measurement and a second analyte measurement,illustrated as the glucose measurement, in measurement region 41. Thedevice compares the measurements to the target range, shown in targetregion 42. The illustrative device 10 may identify the analyte patterntypical for night hypoglycemia and may provide a diagnosis to the user,shown in diagnosis region 43 of the display 19. For example, as shown,the device 10 may conclude that the patient should reduce his eveninginsulin to avoid repetition of a night hypoglycemia, as show by therecommendation 45. In addition, as a consequence of the high FFA in themorning, the device may calculate and inform the patient that, forexample, 50% more insulin will be needed to tackle the increased insulinresistance, as shown in dosage region 44 of the display 19.

The health monitoring device may also be used to identifyover-insulinized patients by measuring FFA levels. The risk exists thatthe patient becomes trapped in a cycle of increasing his insulin eachtime he perceives a high glucose reading. Ignorant about the effects ofthe counter-regulating hormones, a patient may end up with frequent highlevels of both FFA and glucose, and a low insulin sensitivity whileconsuming large amounts of insulin. The medical community has started torecognize this logical self-perpetuating cycle. The only efficient,though intuitively contrary approach is to drastically reduce theinsulin intake to restore the hypoglycemia, reduce the FFA levels andimprove the insulin sensitivity. Therefore, information regarding FFAcombined with glucose levels, as measured and analyzed using the device10, may provide information early to the patient so he can avoidover-insulinization or restore insulin sensitivity.

As shown in FIG. 1, the device 10 of the present invention may be usedto track the evolution of an insulin sensitivity factor in a patient.Depending on the volatility of the insulin sensitivity factor and thetherapeutic goals, the time basis for the tracking can be changed,showing the evolution over weeks or days, rather than months. An(averaged) intra-day evolution can reveal even more detailedinformation. Consistent low insulin sensitivity in the late afternoon,for example, may signal the patient to reduce insulin before lunch orincrease the lunch calorie content or composition.

According to another embodiment of the invention, the device 10 canmeasure and utilize FFA and glucose levels to assess the prospectivedevelopment of hypoglycemia and hyperglycemia in a patient. For example,FIG. 4 shows use of the device 10 of the illustrative embodiment of thepresent invention to inform the user regarding potential imminenthypoglycemia, as shown in FIG. 4. The challenge of the nighthypoglycemia and “dawn phenomenon” treatment is to avoid low glucoselevels overnight primarily by identifying the conditions in advance. FFAlevels in combination with glucose measurements, as detected by thedevice 10, can help to avoid low glucose levels overnight. Certainpatterns, such as a normal to low glucose level in the presence of lowFFA level at bedtime or early night, may indicate the development ofhypoglycemia in the near future. By detecting this pattern, the systemand method of the invention may help the patient to take preventivesteps to avoid imminent hypoglycemia. The device may providerecommendations to the patient, such as to take an extra snack (withslow absorbing carbohydrates) before going to sleep. As shown in FIG. 4,the device 10 graphs both free fatty acid levels 32 and glucose levels33 on the display 19 and compares the measured levels to a therapeuticgoal, illustrated as region 31. The display may display a message 34that warns the user of potential hypoglycemia, based on the measuredanalytes. In FIG. 4, the illustrative device 10 recognizes that althoughthe glucose reading 31 is near normal, the low FFA level 32 indicates astrong insulin activity in spite of the already normal to low glucoselevel.

In the opposite case, when the glucose level is normal to high in thepresence of a high FFA level at bedtime or early night, one might expecta hyperglycemic response. This situation typically occurs wheninsufficient insulin is available. The high FFA level in combinationwith the lack of insulin action may result in keto-acidosis, which canbe a life threatening condition and may lead to a coma. When such acondition exists, the device 10 can detect the condition and mayrecommend to the patient to take extra insulin or reduce food intake tocorrect the condition.

The Correlation of the Analyte Levels to the Real Insulin Activity in aUser:

FIGS. 6-10 relate to use of a health monitoring device of anillustrative embodiment of the invention for the correlation of theanalyte levels to a real insulin activity level and for providing ameasurement indicative of a difference (i.e., insulin activity error)between real insulin levels and theoretical insulin levels according toanother embodiment of the invention. Particular challenges are inherentto regulate glucose levels in Type I diabetes. Type I patients lack therequired insulin production (partially or completely). Such a patient isrequired to dose his insulin accurately and inject himself several timesa day. The dosing of the insulin is based on the diabetic patient'sassumed food intake, his basic metabolic rate, sleep and wake cycle,exercise sessions, episodes of sickness, emotional stress, and otherfactors.

Current algorithms for dosing insulin are based on a theoreticalabsorption (appearance in the bloodstream) of the subcutaneous injectedinsulin dose and are inadequate. To help absorb the carbohydrates andfats from a meal, the patient injects himself with a quantity ofshort-acting insulin prior to each meal. The insulin needed for thebasal metabolism is covered by a once-a-day injection of long-actinginsulin. In the theoretical patient, these interventions keep his bloodglucose levels within physiological limits. However, a series ofunpredictable interferences interact with the insulin absorption and itsactivity, resulting in hypo- and hyper-glycaemic episodes as well asunwanted counter-regulating events.

Therefore, a real insulin activity level in a user is often differentfrom the theoretical level, leading to an inability to accuratelycontrol one's health.

This embodiment of the invention facilitates assessment of thediscrepancy between the “real insulin activity” and the “theoreticalinsulin activity” to provide a parameter known as an “insulin activityerror”, which may then be used to control the patient's insulin moreaccurately and promote the patient's overall health.

The Real Insulin Activity:

The illustrative embodiments of the invention define real insulinactivity as the net insulin effect on glucose and fat metabolism.Different components contribute to this real insulin activity and itsdifference with the theoretical insulin activity, including thevariability in the absorption of insulin, the presence ofcounter-regulating hormones, the presence of anti-insulin antibodies,the clearance of insulin from the circulation, insulin sensitivity ofthe tissues, and other factors. The present invention may calculate andtrack the difference between the real insulin activity and thetheoretical insulin activity in a user to promote the overall health ofthe user.

For the factor of variability of the absorption, there is a greatvariance in absorption of subcutaneously injected insulin. Using theplasma concentration of insulin eliminates the variance in absorption ofthe subcutaneously injected insulin. This variability can be significantdepending on, the site of injection, the nature of the injection site,and the depth of the injection. For example, there is a difference inabsorption between injections to the belly, which tend to be fastabsorbing, and to the leg, which tend to be slow absorbing. In addition,injecting in the same (scarred) skin areas gives a slower absorption.Blood flow and vasodilatation around the injected bolus also varies withemotion, stress and temperature. In addition, insulin injected into themuscle (too deep) will appear faster in the bloodstream than the correctsubcutaneous injection. Therefore, these and other factors affect howquickly the insulin is absorbed, which in turn may cause a discrepancybetween a real insulin activity level and an expected or theoreticalinsulin activity level.

A series of hormones present in plasma, known as counter-regulatinghormones, exert the opposite effect on glucose and fat metabolism. Forexample, Growth hormone, Glucagon, Cortisol, Catecholamines and othershave hyperglycaemic effects on glucose metabolism. They elevate glucoselevels through the release of glucose from the glycogen stores in theliver, the supply of an alternative energy source (FFA from fat depots)and inhibit the storage of glucose. Some of these hormones actionsexplain typical deregulation in Type I diabetes: Stress (catecholaminesand cortisol) coincides often with high glucose levels. In addition,counter-regulating hormones have a particular action on fat metabolism:Lipolysis, or the release of Free Fatty Acids (FFA) into the circulationfor energy supply. Therefore, the presence or absence of such hormonesmay cause a discrepancy between a real insulin activity level and anexpected or theoretical insulin activity level.

A variety of different anti-insulin antibodies to insulin have beendiscovered. They partially or completely inactivate the insulin theybind to, also affecting the real insulin activity level and potentiallycausing a discrepancy between a real insulin activity level and anexpected or theoretical insulin activity level.

In addition, there are individual variations in the clearance of activeinsulin from the bloodstream, which may affect the actual level ofinsulin in the blood.

The individual response to a dose of insulin is also dependent onsensitivity of the body's organs to insulin. Muscle, the main consumerorgan of glucose and FFA, can have a variable response to the same doseof insulin. The sensitivity varies from person to person, in function ofthe severity of the disease and obesitas. The sensitivity also variesduring the day. Some of the underlying causes of insulin resistanceinclude, but are not limited to, that at the muscle level high FFAconcentration inhibits glucose uptake, glycogen synthesis and glucoseoxidation and that the effects will reduce the clearance of glucose fromthe bloodstream, resulting in high glucose levels. More insulin willtherefore be needed to maintain normal glucose levels.

All current methods do not compensate for those interfering factors andrely on the appearance of insulin in plasma based on the theoreticalabsorption curves. The illustrative embodiment of the inventionincorporates all of them by assessing the real insulin activity based onthe measurement of an analyte from fat metabolism and glucosemetabolism, rather than inaccurate estimations.

Assessing the Real Insulin Activity:

In addition, fat tissue metabolites reflect the real insulin activity ina patient. A drop in insulin action and a rise in counter-regulatinghormones tend to cause a rise in Free Fatty Acids (FFA). Adipose tissueplays an important role in energy supply. In the absence of sufficientglucose and insulin to meet the body's energy needs, lipolysis suppliesFree Fatty Acids for energy. Body fat is broken down to release FreeFatty Acids (FFA) and glycerol into the circulation. This typicallyoccurs in the post-absorptive phase (the time span between the digestionof a meal and the start of the next meal) and overnight (the longestfasting period of the 24 H-day). The regulation of lipolysis is undercontrol of a variety of hormones, including insulin, glucagon, growthhormone (GH), epinephrine, adrenalin and cortisol.

An illustrative embodiment of the invention uses FFA measurements toreflect the insulin, counter-regulating hormones and insulin sensitivityto compose the real insulin activity, as shown in FIGS. 8A-8C.

One or more additional parameters, such as body mass index, gender, mealintake, medication, exercise duration and intensity, alcohol consumptionand weight, may be utilized in an algorithm run by the health-monitoringdevice 10 to correct for the insulin activity error calculated in themanner described above. The algorithm correcting for the insulinactivity error may be used to model the glucose levels in the patientfor a future period.

In FIG. 8A, a traditional glucose prediction algorithm (such as DIASnet)is used to predict near term glucose levels 411 in function of theinjected quantity and type of insulin, the theoretical insulinabsorption curves, the meal intake, the theoretical gut absorptioncurves, as well as the duration and intensity of exercise. Thepredictive accuracy is poor of such algorithms. FIG. 8A clearly showsthe measured glucose values 431 deviating from the predicted glucoselevels 411.

This algorithm, in the ideal predictable patient, may predict theobvious hypo- and hyper-glycaemic episodes 441 due to a known mistakesuch as: skipping a meal, injecting too little or too much insulin.

In the real patient, about half the hypo- or hyper-glycaemic episodesare not detected by such algorithm. FIGS. 8A and 8C show threeunpredicted hypoglycaemic episodes 451 that occur in a patient, as ableto be tracked by a health monitoring device of an illustrativeembodiment of the invention, while only two episodes 441 were predictedaccording to traditional methods.

The illustrative embodiment of the invention corrects the classicalglucose prediction models by measuring the FFA levels 461 andconsequently correcting the theoretical insulin activity 421. FIG. 8Bshows the theoretical insulin curve 421 with the real insulin activity471, illustrating a discrepancy between the two levels, as tracked usingthe illustrative health monitoring device. This real insulin activity isa mathematical reciprocal conversion of the FFA levels 461. Themathematical conversion is done for scaling and range purposes. Thereciprocal function is introduced because of the inverse relationshipbetween FFA and insulin activity. The units in the Y-axis are arbitrary.

From the calculations shown in FIG. 8B, the difference between thetheoretical insulin activity 421 and the real insulin activity 471 iscalculated. This difference, the insulin activity error, is shown inFIG. 8C as a curve 481 and can be tracked and displayed on the display19 of the health-monitoring device 10, as shown in FIGS. 6-7B.

As shown in FIG. 8A, the difference between the predicted glucose values411 and the measured glucose values 431 can also be tracked using thedevice of the present invention. This difference, the glucose predictionerror, is shown in FIG. 8C as a curve 491.

As shown in FIG. 8C, the glucose prediction error (GPE) 491 calculatedaccording to the teachings of the invention can be either positive, whenthe measured glucose level is higher than predicted, or negative, whenthe measured glucose level is lower than predicted. A negative GPE doesnot necessarily leads to a hypoglycaemia incident. Rather, theoccurrence of hypoglycaemia depends upon how low the absolute glucoselevel is in the patient.

As shown in FIG. 8C, the health monitoring device may employ the insulinactivity error (IAE) 48 to explain the glucose prediction error 491. Asdescribed above, the “Insulin Activity Error” refers to the differencebetween a real insulin activity and a theoretical insulin activity. Asshown, the unexpected hypo- and hyper-glycemic episodes 451 areanticipated by a significant IAE.

Typically, a strong positive IAE 481 (indicating a higher Real InsulinActivity than in the theoretical model) would lead within 4-6 hours to amuch lower than predicted glucose level (a negative GPE) 491, and viceversa, which can be tracked, predicted and monitored using theillustrative health monitoring device.

Therefore, the calculation of an insulin activity error according to anillustrative embodiment of the invention can be useful in promoting thehealth of a user.

Applications of the Real Insulin Activity and the Insulin Activity Error

Referring to FIG. 9, a home monitor device 10 of an embodiment of theinvention may test for both glucose and FFA's (glycerol or ketonebodies), preferably in a combined strip. Each time the patient test hispre-prandial glucose level, the device may measure the FFA (glycerol orketones) level as well. In one embodiment, only the glucose result isdisplayed in area 52 of the screen 19, while the FFA testing and the FFAvs. insulin algorithm runs in the background, invisible to the patient.However, the FFA levels may also be displayed on the screen 19 inaccordance with the teachings of the invention.

Upon detection of an imminent insulin under- or over-activity, themonitor may advise the patient to increase his testing frequency, asshown in area 51 on the screen 19 of the device. By doing so, thepatient may pick up unexpected high or low glucose levels as they startto develop and prior to any damage to his health.

Inputs to the device to provide the results shown in FIG. 9 may includeglucose and FFA levels, information regarding meals, insulin injections,exercise, and/or alcohol intake. The Glucose and FFA (glycerol orketones) levels are preferably tested four times a day: before each mealand at bedtime. The meal information may include only the carbohydrateintake in bread units or grams, or any suitable parameter. Informationregarding insulin injections may include the type and number of insulinunits.

The output from the device shown in FIG. 9 is preferably advice on thetesting frequency to guide a patient through a critical deregulationperiod. The system may or may not justify the reason of its advice:“Start testing every hour, more insulin activity is coming” or “Starttesting every hour, your glucose may drop more than expected” 51.

As shown in FIG. 10, the health monitoring device may display theglucose result 52 against the therapeutic target range 53 in display 19.In addition, the insulin activity error 54 can also be graphicallypresented against a target range, illustrating to the user that thecombination of a near-normal glucose level together with a high insulinactivity error is becoming dangerous.

The use of a health monitoring device to determine glucose levels, freefatty acid levels, calculate an insulin activity error and/or provideadvice to a patient, as shown in FIG. 6-10, provides significantbenefits to a patient. For example, the user will be alerted in advanceby both predicted and unpredicted low or high glucose levels. Theadvance notice is usually 4 to 8 hours, but can be any suitable timeperiod. The user can decide to take pre-emptive measures. For example,the care taker of a child can make sure the child is put to sleep withnormal glucose levels and a normal insulin activity, a patient can takean additional or a corrected amount of insulin, take more or lesscarbohydrates and/or can decide to take an evening snack or delete it,in response to the output from the device 10.

Alternatively, or in addition, the user can track the deterioratingglucose levels and take appropriate intervention action. For example,the user can seek medical help in time, avoid the keto-acidosis todevelop by treating his hyperglycaemia, and/or treat the hypoglycaemia.

The heath-monitoring device of the present invention may also oralternatively be used to identify over-insulinization. Frequent episodesof hypoglycaemia and the rebound hyperglycemia are often due toover-insulinization. Over-insulinization is detected by finding frequentor consistent episodes of high real insulin activity or negative insulinactivity errors. The patient or health care professional can adapt theinsulin regime for avoiding this very frequent phenomenon.

The device may also empower patients with a low test frequency to takecontrol. Both expected and unexpected low or high glucose levels happenprobably once to twice per 24 hours and are the main reason why patientsfeel that they cannot manage their diabetes. Consequently they test lessbecause they feel unable to “do it right”. They usually test because“their HCP told them to test”. When those patients feel low or high,they experience this as a confirmation of their inability to cope withthe disease.

It is therefore likely that those patients will resume testing whentheir glucose system adds advanced information of what might happen inthe near future so they can now take advanced precautions. The inventioncan empower patients testing only once or twice a day to test 4 times aday. In addition, when patients will follow the test frequency advice,they will be able to avoid a significant number of hypo- andhyper-glycaemic episodes.

The monitoring device may also be used to provide assistance indetermining insulin dosage, based upon both the glucose levels and theFFA levels in the user. For example, as shown in FIG. 10, the device mayidentify hypoglycaemia retrospectively.

As described, glucose levels alone are not ideal to dose insulin.Glucose readings can be normal to very high in the morning as aconsequence of hypoglycaemia overnight. This situation is rather causedby an over-dosing of insulin relative to the meal intake in the evening.These patients with high glucose and high FFA in the morning shouldreduce insulin (or increase caloric intake or change meal composition)in the evening rather than the natural reflex of taking more insulin.Current practice in self-dosing of insulin lacks the counter-regulatinghormone information and works with glucose levels alone. In contrast,the present invention allows more accurate control of glucose levels.

High FFA levels in the morning may indicate a strong counter-regulatinghormone activity. This translates into a low real insulin activity. Thestrong negative insulin activity error together with normal to highglucose level is indicative of a past hypoglycaemic episode, which maybe identified using the device of the present invention.

For example, FIG. 11 shows the display 19 of the health monitoringdevice according to one embodiment when the device is used to displayearly morning test results of glucose and IAE. As shown, the display 19of FIG. 11 displays a first analyte measurement, illustrated as the IAEcalculation, and a second analyte measurement, illustrated as theglucose measurement, in measurement region 61. The device compares themeasurements to the target range, shown in target region 62. The devicemay identify the analyte pattern typical for night hypoglycaemia and mayprovide a diagnosis to the user, shown in diagnosis region 63 of thedisplay 19. For example, as shown, the device may conclude that thepatient should reduce his evening insulin to avoid repetition of a nighthypoglycaemia, as show by the recommendation 64.

In addition, as a consequence of the negative IAE (high FFA) in themorning, the device may calculate and inform the patient that, forexample, 50% more insulin may be needed to tackle the increased insulinresistance, as shown in dosage region 65. However, some modernclinicians may instruct the patient not to increase the insulin dose inthis situation of high glucose levels. The negative IAE is primarily dueto high counter-regulating hormone activity which will fade over thenext 12 hours. The clinician may then want to avoid an excess in insulinactivity which may lead to a new hypoglycaemia.

The use of a device that identifies hypoglycaemia retrospectively alsoprovides significant patient benefits. As described, glucose levelsalone may not be ideal to dose insulin. Glucose readings can be normalto very high in the morning as a consequence of hypoglycaemia overnight.These patients with high glucose and a negative IAE in the morningshould reduce insulin (or increase caloric intake or change mealcomposition) in the evening rather than the natural reflex of takingmore insulin. Current practice in self-dosing of insulin lacks the RealInsulin Activity information and works with theoretical insulin activityalone. As a consequence, the described invention allows patients toavoid the hypoglycaemia overnight by taking the right measures. Insteadof the usual several days to get back into control, a patient employinga health monitoring device of the invention may be back on track by thenext evening.

The increased insulin resistance in the morning following ahypoglycaemia, as quantified in the IAE can now be used to adjust theinsulin dose in the morning. A strong negative IAE indicates a higheramount of insulin will be needed to keep glucose levels within thenormal rage. Alternatively, the user may be advised, as modernclinicians now advise, not increase the insulin dose, since the insulinactivity error will normalise over the course of the day when thecounter-regulating hormones fade out.

Referring again to FIGS. 7A and 7B, the insulin activity errorcalculated using the device of an illustrative embodiment of theinvention may be used to identify over-insulinized patients. The riskexists that the patient may become trapped in a cycle of increasing hisinsulin each time he perceives a high glucose. Ignorant about theeffects of the counter-regulating hormones, a patient may end up withfrequent negative IAE's and high glucose while consuming large amountsof insulin, creating a self-perpetuating cycle. The only efficient,though intuitively contrary approach is to drastically reduce theinsulin intake to restore the normo-glycemia, reduce the FFA levels andminimize the IAE, which may be advised when employing a device accordingto the teachings of the invention.

Therefore, the insulin activity error 230 (the difference between thereal insulin activity 210 and the theoretical insulin activity 201) ascalculated from FFA levels and injected insulin, may provide informationearly to the patient so he can avoid over-insulinization or restore it.

As shown in FIGS. 7A and 7B, the device 10 of the present invention maybe used to track the evolution of the insulin activity error 230.Depending on the volatility of the IAE and the therapeutic goals, thetime basis can be changed, showing the evolution over weeks or months240 rather than days.

An (averaged) intra-day evolution can reveal even more detailedinformation. Consistent negative IAE's in the late afternoon, forexample, may signal to reduce insulin before lunch or increase the lunchcalorie content or composition.

According to another embodiment, the calculated insulin activity errormay be used as a therapeutic goal. For example, in patients withconsistently elevated glucose values and negative IAE, the improvementof the IAE can become a therapeutic objective.

The health-monitoring device 10 of an illustrative embodiment of thepresent invention may be used to measure and interpret the IAE of apatient, taken at certain moments of the day and at certain intervals,as well as the glucose level of the patient. Based on the measurement ofthe FFA level, the glucose level and the injected insulin, the devicecalculates and displays the IAE as shown in FIG. 7B. The progression ofthe IAE can be displayed in relation to the set therapeutic objective.The objective can be displayed as a progressively tightening zone 250 toallow for flexibility and time to achieve success of the therapy.

In individuals with marginally abnormal glucose values, such as inobesitas or onset metabolic syndrome that do not yet take externalinsulin, the system and method of the present invention may measure thevariable FFA levels only. The system will display the real insulinactivity rather than insulin activity error. Therapeutic interventions,such as losing weight, taking insulin sensitizers or any othermedication to improve insulin sensitivity, may show a clear improvementof the Real Insulin Activity.

The health monitoring device of the invention may also be used topredict glucose levels more accurately. Glucose prediction algorithmsaim at predicting glucose levels based upon the meal, with or withoutthe specifics of the meal composition, the amounts and types of theinjected insulin and the glucose values. More complex algorithms alsouse inputs including, but not limited to, exercise duration andintensity, glycaemic index of the meal, alcohol consumption. Bypredicting glucose levels, the patient may decide to change his mealintake or quantity of insulin to inject, trying to keep glucose withinnear-normal levels.

A major draw back of the algorithms used to predict glucose levels inthe prior art is that the algorithms use theoretical absorption curvesof the injected insulin. These curves try to predict the appearance ofinsulin in the bloodstream and the insulin activity which is then usedin the prediction model. The current prediction algorithms do not takeinto account other insulin-interfering factors. Such factors include,but are not limited to the variability of the absorption depending onthe site of injection or the injection, the effects of thecounter-regulating hormones, such as cortisol, catecholamines, growthhormone, glucagons, insulin sensitivity of the various organs, which ismainly driven by the concentration of FFA, the presence and inhibitingactions of anti-insulin antibodies and the variability in the clearanceof insulin. For example, absorption may be fast when injected in bellyfat, slow when injected in the leg, and scarred tissue may result inretarded absorption.

The illustrative embodiments of the invention, by measuring FFA togetherwith glucose and calculating the insulin activity error, may improvedramatically the accuracy of those algorithms. The invention allows useof the real insulin activity rather than the theoretical insulinactivity to increase accuracy.

According to another application, the device 10 utilizes FFA and glucoselevels to assess the insulin sensitivity and therefore help indetermining an appropriate pre-prandial insulin dose. The combinedinformation of glucose and FFA levels in a patient allow the device 10to assess the insulin sensitivity of the patient. Information regardinga patient's insulin sensitivity can be particularly relevant when thepatient has to inject himself with insulin prior to his meal. Theinsulin is intended to remove glucose from the bloodstream that appearsas a result of the meal digestion. When high levels of FFA are presentresulting in a low insulin sensitivity, the user must to inject himselfwith a higher amount of insulin to avoid high glucose levels after themeal. This is particularly helpful before breakfast when FFA levels areusually high.

According to another application, the health-monitoring device mayutilize information regarding FFA and glucose levels for closed loopsystems and insulin dosing algorithms. Glucose alone as a reflection ofcarbohydrate metabolism has proven to be insufficient to build reliabledosing algorithms. Systems based on glucose input alone are lacking theessential information from the fat metabolism, counter-regulatinghormones and insulin sensitivity. FFA levels combined with glucose, asset forth in the present invention, provide a more complete picture ofthe actual metabolic state of the patient. The present inventioncombines the fat and glucose metabolic information as inputs for insulindosing algorithms. These algorithms may be stand-alone minicomputer orpalmtop based systems as well as incorporated in glucose measurementdevices or insulin delivery systems (i.e. insulin pen or pump). Some ofthose algorithms are predictive in such that they assess the expectableglucose levels in the near future.

Closed loop systems are systems that aim to deliver insulinautomatically based on inputs from the patient's metabolic state. Theyconsist typically of a measuring or input device for metabolicparameters (i.e., glucose, dietary input, logging exercise and insulinadministration), an insulin dosing algorithm and an insulin deliverysystem.

EXEMPLIFICATION OF THE INVENTION

A home study was conducted on five diabetic patients (RW, KP, JC, MJ andNO), all type I. The home study was conducted over two separate weeks,during which tests were conducted, tracking insulin levels and glucoselevels in the patients' blood. Two diabetic patients (RW and KP) weretested twice, producing two sets of results for these patients.

During testing, Microtainer tubes were used to collect a blood sampledusing a finger stick for Free Fatty Acids, b-Hydroxybutyrate. Familiarhome monitoring device for glucose testing were used. Sample collectionwas instructed to be done just before each meal and insulin injection. Afourth sample of the day was taken at bedtime. Samples were then storedby patient at home in the refrigerator. Collection was next morning bytaxi service for appropriate storage in the lab and analysis.

FIGS. 12A-18B are graphs shown the results of the tests done for eachtest involving a patient. FIGS. 12A, 13A, 14A, 15A, 16A, 17A and 18Aillustrate the results for each patient, with FIG. 13A representing thesecond testing of patient “RW” and FIG. 15A representing the secondtesting of patient “KP”. FIGS. 12B, 13B, 14B, 15B, 16B, 17B and 18B arecharts diagramming the glucose prediction error and insulin activityerror based on the test results for each patient.

In addition to providing blood samples, patients registered carbohydrateintake and insulin injections. The carbohydrate intake and insulininjections were used to construct a glucose prediction curve 130 a-130g, shown in FIGS. 12A, 13A, 14A, 15A, 16A, 17A and 18A, according to thealgorithm of DIASnet. The generated glucose prediction curves showexpected glucose values based on the theoretical action profiles of theinjected insulin.

The error between the predicted glucose and measured glucose was definedgraphically as lines 122 a-122 g, shown in FIGS. 12B, 13B, 14B, 15B,16B, 17B and 18B. In addition, the unexplained difference between FFAand pre-prandial insulin levels was defined graphically, as shown bylines 152 a-152 g in FIGS. 12B, 13B, 14B, 15B, 16B, 17B and 18B.

The measured glucose levels (lines 120 a-g, respectively), the predictedglucose levels (lines 130 a-g, respectively), the free fatty acid levels(lines 140 a-g, respectively) and the insulin activity levels (lines 150a-g, respectively) were charted for each patient over the testingperiod. FIGS. 12A, 13A, 14A, 15A, 16A, 17A and 18A illustrate theresults for each patient, with FIG. 13A representing the second testingof patient “RW” and FIG. 15A representing the second testing of patient“KP”. FIGS. 12B, 13B, 14B, 15B, 16B, 17B and 18B chart the glucoseprediction error (lines 122 a-g, respectively), the insulin activityerror (lines 152 a-g, respectively), the incidents of predictedhypoglycaemia (lines 160 a-g, respectively) based on the measurements ofglucose levels using traditional methods, free fatty acid levels andinsulin activity levels, and the incidents of unexpected or unpredictedhypoglycaemia (lines 170 a-e, respectively) that occurred during eachstudy.

The glucose prediction error curve 122 a-g charts the error between themeasured glucose and the predicted glucose during each testing periodfor each patient, respectively. The insulin activity error curve 152 a-gcharts the deviation of the free fatty acid levels with regard toexpected theoretical insulin activity for each testing period for eachpatient, respectively. During an analysis, the peaks in the insulinactivity error were compared with the effect on the glucose predictionerror. It was found that strong negative glucose prediction errors wereusually preceded by strong positive peaks in the insulin activity errorcurve. When the predicted glucose was already low, they typically leadto hypoglycaemia.

As illustrated in the summary charts, in FIGS. 12B, 13B, 14B, 15B, 16B,17B and 18B, an unexpected hypoglycaemia incident is typically preceded(up to 6 hours in advance) by a positive Insulin-Activity-Error (IAE).This is also valid for any negative glucose prediction error (GPE).Conversely, a positive GPE is usually preceded by a negative IAE. Thehealth-monitoring device 10 of the illustrative embodiments of theinvention may utilize this tracking to promote and improve the user'shealth.

As also shown in the summary charts, there are two hypoglycaemic eventspossible: the predicted hypoglycaemic events, indicated by lines 160 a-gand the unpredicted hypoglycaemic events, indicated by lines 170 a-g.The predicted hypoglycaemic events 160 a-g usually result from injectingtoo much insulin or eating too little, and are easy to prevent bylooking at the prediction curve and take appropriate pre-emptive action,such as eating more or injecting less. The unpredicted hypoglycaemicevents 170 a-g result from too much insulin activity liberated from theinjection site earlier than expected (accelerated release) or later andmore than expected (retarded release with accumulation of the previousinjection dose). The unpredicted hypoglycaemic events 170 a-g can alsoresult when pre-existing insulin resistance or counter-regulatinghormone activity can have faded.

The curves show that most hypoglycaemic and hyperglycaemic events areunpredicted by the prediction curves of traditional models.

In addition, hyperglycaemic events that may be caused by too littleInsulin activity or excess in counter-regulating hormones and/or insulinresistance (which is to the greater part caused by the high FFA levels),are also often unpredicted.

A positive IAE (more suppressed lipolysis with low FFA's thanexplainable by the theoretical insulin absorption curves) may lead to anegative GPE (lower glucose levels than predicted) within 4-6 hours.Similarly, less than expected insulin activity can lead to higher thanpredicted glucose levels.

The illustrative embodiment of the invention employs these results toincrease the accuracy of traditional glucose prediction algorithms. Theresults may be explained through the interaction between differenthormones. Lipolysis is under the control of the same hormones ascarbohydrate metabolism. Fat tissue is very sensitive to the actions ofinsulin. Meals do not affect FFA levels in the same way that they alterglucose levels. Insulin, glucagon, growth hormone, adrenergic hormones,and other hormones all regulate in a combined fashion lipolysis. Theresults show that FFA levels are reliable for assessing the effectiveinsulin activity. The absorption of the insulin depot at the injectionsite is highly variable and rather unpredictable, especially the longerthe action profile of the insulin is. FFA levels give a more accuratepicture of the combine hormonal activity (including the insulinresistance) than the assumed theoretical absorption curves. Currently,by testing glucose alone, the hypoglycaemic (and hyperglycaemic)episodes are usually not predictable. Using the method of testing FFAand Glucose in accordance with the teachings of the invention enablesdetection of over or under activity of insulin and counter regulatinghormones which may lead to hypo- or hyperglycaemia.

The examples confirm that the invention is far more accurate inpredicting hyper- and hypo-glycaemia. Correcting a traditional glucoseprediction algorithm with this invention predicted twice as manyhypoglycaemic episodes than would be predicted using traditionalalgorithms.

Therefore, the invention facilitates monitoring, treatment, managementand improvement of the overall health of a user by allowing a user tomore accurately control glucose levels, assess a real insulin activitylevel of the user, track an insulin activity error, be warned aboutimminent hypoglycaemia or hyperglycaemia, receive formulated insulindosage from the device, receive advice from the device and so on.

The present invention has been described relative to an illustrativeembodiment. Since certain changes may be made in the above constructionswithout departing from the scope of the invention, it is intended thatall matter contained in the above description or shown in theaccompanying drawings be interpreted as illustrative and not in alimiting sense.

It is also to be understood that the following claims are to cover allgeneric and specific features of the invention described herein, and allstatements of the scope of the invention which, as a matter of language,might be said to fall therebetween.

What is claimed is:
 1. A computer-implemented method of assessing aninsulin activity error and its effect on glucose levels, the methodcomprising the computer-implemented steps of: measuring an amount of afat metabolism analyte in a biological fluid sample and an amount of aglucose metabolism analyte in the biological fluid sample, determining,using a computer, a real insulin activity level, wherein the realinsulin activity level comprises a reciprocal conversion of the amountof the fat metabolism analyte in the biological fluid sample, comparing,using the computer, the real insulin activity level with a theoreticalinsulin activity level to calculate the insulin activity error; andpredicting, using the computer, a prospective glucose level using theamount of the glucose metabolism analyte in the biological fluid and thecalculated insulin activity error, wherein a positive insulin activityerror is predictive of a glucose level which is lower than thatpredicted by the theoretical insulin activity level, and wherein anegative insulin activity error is predictive of a glucose level whichis higher than that predicted by the theoretical insulin activity level.2. The method of claim 1, wherein the fat metabolism analyte is selectedfrom the group consisting of ketones, glycerol, free fatty acids and afatty acid indicative of free fatty acid levels.
 3. The method of claim1, wherein the glucose metabolism analyte is selected from the groupconsisting of glucose, pyruvate, glucose-6-phosphate and lactate.
 4. Themethod of claim 1, wherein the sample comprises one of blood, a derivateof blood, interstitial fluid, urine and saliva.
 5. The method of claim1, wherein the step of predicting a prospective glucose level comprisespredicting a likelihood of a user developing one of hypoglycaemia andhyperglycaemia.
 6. The method of claim 1, wherein the method comprises aretrospective diagnosis of one of hypoglycaemia and hyperglycaemia. 7.The method of claim 1 wherein the method comprises recommending a timingfor a future determination of analyte levels in a user.
 8. Acomputer-implemented method of predicting a user's glucose levels in thefuture, the method comprising the computer-implemented steps of:measuring an amount of a fat metabolism analyte in a biological fluidsample and an amount of a glucose metabolism analyte in the biologicalfluid sample, determining, using a computer, a real insulin activitylevel in the user, wherein the real insulin activity level comprises areciprocal conversion of the amount of the fat metabolism analyte in thebiological fluid sample; comparing, using the computer, the real insulinactivity level with a theoretical insulin activity level to calculate aninsulin activity error; and running, using the computer, a glucoseprediction algorithm corrected for the insulin activity error, whereinthe correction comprises decreasing a glucose level predicted by thetheoretical insulin activity when the insulin activity error is positiveand increasing a glucose level predicted by the theoretical insulinactivity when the insulin activity error is negative, the algorithmtaking into consideration an additional parameter comprising at leastone of: body mass index, gender, meal intake, medication, exerciseduration and intensity, alcohol consumption and weight.
 9. The method ofclaim 8, further comprising the step of: calculating, using thecomputer, a likelihood of the user developing one of hypoglycaemia andhyperglycaemia.
 10. The method of claim 8, further comprising the stepof: calculating, using the computer, a likelihood of one of a pasthypoglycaemia incident and a past hyperglycaemia incident.
 11. Themethod of claim 8, further comprising the step of: formulating testfrequency advice to the user.
 12. The method of claim 8, furthercomprising the step of: formulating medication dosing advice to theuser.
 13. The method of claim 8, further comprising the step of:automatically communicating insulin dose information to an insulindelivery device.
 14. A health-monitoring device comprising: a testelement configured to receive a biological fluid sample and measure: anamount of a fat metabolism analyte in the biological fluid sample, andan amount of a glucose metabolism analyte in the biological fluidsample; a processor programmed to: assess a real insulin activity levelof a user, wherein the real insulin activity level comprises areciprocal conversion of the amount of the fat metabolism analyte in thebiological fluid sample, compare the real insulin activity level with atheoretical insulin activity level to calculate an insulin activityerror, and predict a prospective glucose level using the amount of theglucose metabolism analyte in the biological fluid and the calculatedinsulin activity error, wherein a positive insulin activity error ispredictive of a glucose level which is lower than that predicted by thetheoretical insulin activity level, and wherein a negative insulinactivity error is predictive of a glucose level which is higher thanthat predicted by the theoretical insulin activity level; and a displaydevice for displaying the real insulin activity level, the insulinactivity error, or the prospective glucose level.
 15. The device ofclaim 14, wherein the test element comprises one or more of aphotometric, reflectometric, electrochemical and fluorescence basedsystem for analyzing the biological fluid sample.
 16. The device ofclaim 14, wherein a single test element measures both an amount of a fatmetabolism analyte in the biological fluid sample and an amount of aglucose metabolism analyte in the biological fluid sample.
 17. Thedevice of claim 14, wherein the device automatically or continuouslymeasures the glucose metabolism analyte.
 18. The device of claim 14,wherein the device automatically or continuously measures the fatmetabolism analyte.
 19. The device of claim 14, further comprising: amemory element for tracking the evolution of one of the real insulinactivity level and the insulin activity error for the user.
 20. Themethod of claim 1, wherein the fat metabolism analyte is a ketone andthe glucose metabolism analyte is glucose.
 21. The method of claim 1,wherein the biological fluid sample is an extracted biological fluidsample.
 22. The device of claim 14, wherein the test element comprisesan electrochemical based system for analyzing the biological fluidsample.
 23. The device of claim 22, wherein the test element comprises atest strip.
 24. The device of claim 14, wherein the test elementcomprises a test strip.
 25. The device of claim 16, wherein the testelement comprises a test strip.
 26. The device of claim 14, wherein thetest element comprises a skin-inserted device.
 27. The device of claim14, wherein the test element comprises a non-invasive measurementdevice.
 28. The method of claim 8, wherein the measuring is performedwith a test element configured to contact the biological fluid sample.29. The method of claim 28, wherein the test element comprises a teststrip.
 30. The method of claim 29, wherein the test strip is anelectrochemical-based test strip.
 31. The method of claim 30, whereinthe test strip is configured to measure both the amount of the fatmetabolism analyte and the amount of the glucose metabolism analyte. 32.The method of claim 28, wherein the test element comprises askin-inserted device.
 33. The method of claim 28, wherein the testelement comprises a non-invasive measurement device.